def qualitative_CNN(self, vocab_size, emb_dim, max_len, nb_filters):
self.max_len = max_len
max_features = vocab_size
filter_lengths = [3, 4, 5]
print("Build model...")
self.qual_model = Graph()
self.qual_conv_set = {}
'''Embedding Layer'''
self.qual_model.add_input(
name='input', input_shape=(max_len,), dtype=int)
self.qual_model.add_node(Embedding(max_features, emb_dim, input_length=max_len, weights=self.model.nodes['sentence_embeddings'].get_weights()),
name='sentence_embeddings', input='input')
'''Convolution Layer & Max Pooling Layer'''
for i in filter_lengths:
model_internal = Sequential()
model_internal.add(
Reshape(dims=(1, max_len, emb_dim), input_shape=(max_len, emb_dim)))
self.qual_conv_set[i] = Convolution2D(nb_filters, i, emb_dim, activation="relu", weights=self.model.nodes[
'unit_' + str(i)].layers[1].get_weights())
model_internal.add(self.qual_conv_set[i])
model_internal.add(MaxPooling2D(pool_size=(max_len - i + 1, 1)))
model_internal.add(Flatten())
self.qual_model.add_node(
model_internal, name='unit_' + str(i), input='sentence_embeddings')
self.qual_model.add_output(
name='output_' + str(i), input='unit_' + str(i))
self.qual_model.compile(
'rmsprop', {'output_3': 'mse', 'output_4': 'mse', 'output_5': 'mse'})
def __init__(self, output_dimesion, vocab_size, dropout_rate, emb_dim, max_len, nb_filters, init_W=None):
self.max_len = max_len
max_features = vocab_size
vanila_dimension = 200
projection_dimension = output_dimesion
filter_lengths = [3, 4, 5]
self.model = Graph()
'''Embedding Layer'''
self.model.add_input(name='input', input_shape=(max_len,), dtype=int)
if init_W is None:
self.model.add_node(Embedding(
max_features, emb_dim, input_length=max_len), name='sentence_embeddings', input='input')
else:
self.model.add_node(Embedding(max_features, emb_dim, input_length=max_len, weights=[
init_W / 20]), name='sentence_embeddings', input='input')
'''Convolution Layer & Max Pooling Layer'''
for i in filter_lengths:
model_internal = Sequential()
model_internal.add(
Reshape(dims=(1, self.max_len, emb_dim), input_shape=(self.max_len, emb_dim)))
model_internal.add(Convolution2D(
nb_filters, i, emb_dim, activation="relu"))
model_internal.add(MaxPooling2D(
pool_size=(self.max_len - i + 1, 1)))
model_internal.add(Flatten())
self.model.add_node(model_internal, name='unit_' +
str(i), input='sentence_embeddings')
'''Dropout Layer'''
self.model.add_node(Dense(vanila_dimension, activation='tanh'),
name='fully_connect', inputs=['unit_' + str(i) for i in filter_lengths])
self.model.add_node(Dropout(dropout_rate),
name='dropout', input='fully_connect')
'''Projection Layer & Output Layer'''
self.model.add_node(Dense(projection_dimension, activation='tanh'),
name='projection', input='dropout')
# Output Layer
self.model.add_output(name='output', input='projection')
# loss function by oneself
self.model.compile('rmsprop', {'output': 'mse'})
